用联邦学习检测船舶轨迹异常，保护隐私还省通信开销
Federated Learning for Anomaly Detection in Maritime Movement Data
- 基于联邦学习构建船舶轨迹异常检测模型
- 相比中心化模型，通信成本降低且精度相当
- 适合航运监管、海事安全等需隐私保护的场景
本文提出M3fed，一种用于船舶移动异常检测的新型联邦学习方案。该方法在保障数据隐私的同时，显著降低机器学习过程中的通信开销。我们设计了新的联邦学习策略训练M3fed，并以船舶自动识别系统（AIS）数据为例进行实验。通过对比经典集中式M3模型与新型联邦M3fed模型在通信成本和模型性能上的表现，验证了其有效性。
原文摘要 · Abstract (English)
This paper introduces M3fed, a novel solution for federated learning of movement anomaly detection models. This innovation has the potential to improve data privacy and reduce communication costs in machine learning for movement anomaly detection. We present the novel federated learning (FL) strategies employed to train M3fed, perform an example experiment with maritime AIS data, and evaluate the results with respect to communication costs and FL model quality by comparing classic centralized M3 and the new federated M3fed.
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